AI Modernization Senior Lead Software Engineer

Job Description

Be an integral part of an agile team that works to enhance, build, and deliver trusted market‑leading technology products in a secure, stable, and scalable way. Drive significant business impact through deep technical expertise and problem‑solving methodologies across multiple technologies and applications.

• Builds and operates the spec generation pipeline — Implement artifact ingestion (COBOL source, JCL, job schedules, DB2 schemas, SME‑captured knowledge), chunking strategies, and RAG pipelines that produce structured calculation and workflow specifications validated by domain experts.
• Develops agentic workflows for code translation and migration — Design, implement, and iterate on multi‑agent systems that translate legacy logic into target‑state code (Kotlin/JVM). Build orchestration layers, tool‑use patterns, and guardrails that ensure output correctness for financial calculations.
• Builds evaluation and verification infrastructure — Create automated test harnesses that compare migrated calculation outputs against legacy results. Implement parity testing frameworks, regression suites, and confidence scoring to gate production cutover decisions.
• Contributes to the standard calculation runtime — Help build and extend the target platform that migrated calculations deploy into. Ensure the runtime supports deterministic, immutable, auditable execution.
• Partners with domain SMEs — Embed with mainframe subject‑matter experts across Credit, Money Market & Mutual Funds, Statements & Tax, and IBOR to validate agent outputs, refine prompt strategies, and close knowledge gaps in specifications.
• Extends ETL and CDC pipelines for agent workflows — Build and integrate event sourcing, CDC (change data capture), and data pipelines that support end‑to‑end migrated workflows, including upstream/downstream dependency mapping.
• Operates AI systems in production — Own LLMOps for the toolchain: deployment, monitoring, cost management, latency optimization, token budget management, and incident response. Ensure reliability and compliance for 24/7 operation.
• Iterates rapidly and ships continuously — Work in tight build‑measure‑learn cycles. Prototype quickly, instrument everything, and make data‑driven decisions about agent architectures, model selection, and prompt strategies.
• Contributes to shared tooling and infrastructure — Build reusable libraries, evaluation harnesses, prompt templates, and orchestration patterns that scale AI capabilities across all four core processing domains.
• Drives adoption and governance of approved AI‑assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI‑assisted code review/refactoring, test acceleration, release readiness, incident/root‑cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI‑assisted development and automation capabilities, to improve the value realized by automation at scale.

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Hands‑on experience building LLM‑based applications — agentic architectures, RAG pipelines, prompt engineering, and evaluation frameworks
• Strong software engineering fundamentals: distributed systems, event‑driven architectures, API design, testing practices, and cloud platforms (AWS/EKS/ECS)
• Expert proficiency with AI‑assisted development tools (Claude Code, Git Hub Copilot, Cursor) as core daily workflow
• Strong experience with Python development in production environments
• Demonstrated ability to operate and debug complex systems — you own what you ship
• Clear communicator who can articulate technical trade‑offs to both engineers and business stakeholders
• Demonstrated experience leading effective use of enterprise‑authorized AI‑assisted software development tools within the work environment (e.g., for coding, code review,…